Company

A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.

Role

As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.

This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.

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Focus

Build and ship AI features end-to-end (model â system â user experience)
Design and iterate on prompts, tools, memory, and agent workflows
Turn raw model outputs into structured, reliable, and predictable behaviors
Debug issues across the full stack (model, orchestration, infra, UX)
Optimize for latency, cost, and production reliability
Develop lightweight evaluation frameworks to measure real-world performance
Work closely with product and engineering to translate ambiguous problems into working systems

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Tech Stack

Python
PyTorch / JAX
LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
Inference / serving (e.g. vLLM)
Vector DB

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Ideal Experience

Strong foundation in machine learning and modern neural network architectures.
Hands-on experience with training, fine-tuning, or deploying ML models
Ability to write clean, production-quality code
Comfort working across abstraction layers (model â infra â product)
Strong problem-solving skills in ambiguous, fast-moving environments
Bias toward shipping, iteration, and continuous improvement

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Outcomes

ML models in production meet expected accuracy, latency, and reliability targets.
Production issues are identified quickly, debugged effectively, and root causes addressed.
Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
Iterations on models and systems are driven by real-world signals and measurable improvements.

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How We Work

Remote Jobs

Applied AI Engineer

Senior ML Engineer

Administrative Assistant

Applied AI Engineer

Senior ML Engineer

Administrative Assistant